Journal pone 0066296 8

The Socioeconomic and Institutional Determinants ofParticipation in India’s Health Insurance Scheme for thePoorArindam Nandi1, Ashvin Ashok1, Ramanan Laxminarayan1,2*

1 The Center for Disease Dynamics, Economics & Policy, Washington D.C., United States of America, 2 Public Health Foundation of India, New Delhi, India

Abstract

The Rashtriya Swasthya Bima Yojana (RSBY), which was introduced in 2008 in India, is a social health insurance scheme thataims to improve healthcare access and provide financial risk protection to the poor. In this study, we analyse thedeterminants of participation and enrolment in the scheme at the level of districts. We used official data on RSBY enrolment,socioeconomic data from the District Level Household Survey 2007–2008, and additional state-level information on fiscalhealth, political affiliation, and quality of governance. Results from multivariate probit and OLS analyses suggest thatpolitical and institutional factors are among the strongest determinants explaining the variation in participation andenrolment in RSBY. In particular, districts in state governments that are politically affiliated with the opposition or neutralparties at the centre are more likely to participate in RSBY, and have higher levels of enrolment. Districts in states with alower quality of governance, a pre-existing state-level health insurance scheme, or with a lower level of fiscal deficit ascompared to GDP, are significantly less likely to participate, or have lower enrolment rates. Among socioeconomic factors,we find some evidence of weak or imprecise targeting. Districts with a higher share of socioeconomically backward castesare less likely to participate, and their enrolment rates are also lower. Finally, districts with more non-poor households maybe more likely to participate, although with lower enrolment rates.

Citation: Nandi A, Ashok A, Laxminarayan R (2013) The Socioeconomic and Institutional Determinants of Participation in India’s Health Insurance Scheme for thePoor. PLoS ONE 8(6): e66296. doi:10.1371/journal.pone.0066296

Editor: Tiziana Leone, London School of Economics, United Kingdom

Received November 20, 2012; Accepted May 8, 2013; Published June 21, 2013

Copyright: � 2013 Nandi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors hereby declare that this study is independent, and not supported by any grant. The employers of the authors had no role in study design,data collection and analysis, decision to publish, or preparation of the manuscript. Also, there are no current external funding sources for this study.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected]

Introduction

Despite rapid economic growth that has lifted millions out of

poverty in recent decades; the slow pace of improvement in the

health of India’s population has generated a fierce debate on

India’s health policy [1]. Three-quarters of healthcare spending in

India, or $14.5 billion in 2005 [2], is borne out of pocket [3]. Out

of pocket health spending is a major cause of impoverishment,

pushing approximately 3.5% to 6.2% of the population below the

poverty line every year [2,4,5,6,7].

The appropriate balance between supply- and demand-side

initiatives to improve health outcomes is still a subject of

considerable debate. India has historically focused on supply-side

health policies, from the Rural Health Policy (1977) and its

grassroots-level community health workers, to the comprehensive

National Rural Health Mission (NRHM) of 2005 that provided

additional resources for healthcare delivery in poorer states.

Recently, a report of the Planning Commission’s High Level

Expert Group (HLEG) on universal health coverage in India [8]

has recommended a supply-side programme that would assimilate

the current piecemeal supply- and demand-side policies into a

streamlined National Health Package (NHP). It envisions a system

where every citizen would have full access to free healthcare from

either a public healthcare provider or a private provider working

under a government contract. The proposed system would

resemble the National Health Service in the United Kingdom,

with primary, secondary, and tertiary care financed by the

national government but provided by a combination of govern-

mental and non-governmental agencies.

However, during the past decade, the government has

experimented with demand-side policies that complement service

delivery. Following the success of the Janani Suraksha Yojana (Safe

Motherhood Scheme), a conditional cash transfer program for

pregnant mothers [9], the Rashtriya Swasthya Bima Yojana (RSBY,

National Health Insurance Program) was introduced in 2008. The

main goal of RSBY is to encourage access to healthcare, especially

in areas where public health facilities are either unavailable, of

poor quality, or overburdened.

While public health facilities are cheaper (in terms of out-of-

pocket expenditure) and physically near many villages, poor

quality of care, high rates of absenteeism, shortage of equipment

contributes to the lack of healthcare access. These shortcomings

have made private practitioners the dominant treatment providers

to the poor, especially in rural areas. For example, an ongoing

mapping study (Medical Advice Quality and Availability in Rural

India, or MAQARI) finds that more than 92% of rural households

visit private providers, and 79% visit providers with no formal

medical training [10]. RSBY’s approach is to insure the poor

against health shocks by providing them with access to both public

healthcare, and more expensive private healthcare, which they

could not otherwise afford. Thus, it will serve the dual purpose of

PLOS ONE | www.plosone.org 1 June 2013 | Volume 8 | Issue 6 | e66296

improving healthcare access while providing financial protection

to the poor [11,12]. In fact, some also argue that the incentives

embedded in the RSBY scheme can themselves address some of

the system’s deficits by inducing private healthcare supply in poor

and rural markets [13].

There is an ongoing debate about the merits of RSBY. For

example, RSBY only covers inpatient services, (which are typically

conducted at tertiary or secondary healthcare facilities) but does

not cover primary or outpatient care [14]. However, pilot studies

on outpatient coverage under RSBY are underway in two districts

of Orissa and Gujarat (according to RSBY Connect newsletter,

February 2012, Government of India). In addition, there are

concerns about RSBY’s administrative functionality and economic

impact. Seshadri et al. (2012), from a survey of poor households in

Gujarat, found that while hospitalization and utilization rates

among RSBY beneficiaries were much higher compared to non-

beneficiaries, there was no significant difference in the out-of-

pocket medical expenditure between the two groups [15]. There

were also serious administrative inefficiencies, e.g. in households

who were already enrolled under the scheme, 30% of individual

members were not enlisted, making those persons ineligible for

benefits. Furthermore, due to concerns about the reimbursement

process, many healthcare providers had reduced or stopped

offering non-surgical procedures. Another study, Rajasekhar et al.

(2011) found similar problems in Karnataka, such as almost zero

RSBY utilization rates, partly due to lack of information among

beneficiaries, but also due to refusal by care providers [16].

Various other studies have evaluated the economic impact and the

long term financial viability of RSBY [17–20]. On the other hand,

among similar schemes at the state level, a significant reduction in

out-of-pocket medical expenditure among health insurance

beneficiaries in Andhra Pradesh has been found [21].

Although RSBY’s overall success in enrolment is widely

acknowledged [14,22], there remain widespread disparities in

enrolment. In this study, we analyse the factors contributing to

variations in both participation and enrolment in RSBY across

districts.

Although RSBY is mandated by the central government,

decisions to participate in the scheme, and the rate of enrolment,

may depend on various local socioeconomic, political, and

institutional factors. For example, as the central and state

governments (75% central funding, and 25% state funding, except

for Jammu and Kashmir and North-eastern states where the ratio

is 90% central funds and 10% state funds) jointly finance RSBY,

the significant outlay expected of state governments may influence

their decision to participate. Also, enrolment in RSBY is based on

a list of officially poor households (below poverty level or BPL),

which is prepared at the district level. Therefore, district level

factors could affect uptake rates. Furthermore, since there are no

mandates on the implementation of the scheme in the state (e.g. no

definite timeline to complete enrolment), understanding the nature

of participation and enrolment can shed some light on both the

administrative and political bottlenecks, and the household level

determinants of RSBY enrolment. Inequality in participation and

enrolment can also help identify segments not reached by the

program, and focus policymakers’ attention on prioritizing these

segments. The yet uncovered population can be targeted with

enhanced enrolment campaigns, and any barriers to entry for

additional private providers can be removed.

Using RSBY administrative data on enrolment, matched

district-level data from the District Level Household Survey

2007–2008, and data on state fiscal health, political affiliation and

quality of governance, we examine the socioeconomic and

institutional determinants of the participation and enrolment in

RSBY. We find that political and institutional factors are among

the strongest determinants of both participation and enrolment. In

particular, we find that states with pre-existing health insurance

schemes for the poor, and counter-intuitively, states in better fiscal

health are less likely to participate in RSBY.

Since centre-state politics may play an important role, we

examine factors such as the political affiliation of the ruling party,

and control for the quality of governance. We find that these

factors significantly affect both participation and enrolment rates.

Surprisingly, districts located in states that are neutral, or closely

affiliated to the opposition parties at the centre, are significantly

more likely to implement RSBY, and yield a higher enrolment

rate, versus districts in states ruled by parties that are part of the

ruling United Progressive Alliance (UPA) coalition at the centre.

The effect is stronger for districts in states that are part of the

opposition at the centre. Finally, we find some correlation between

socioeconomic household specific factors (such as religion, wealth,

and gender of household head) and RSBY participation and

enrolment.

Description of the RSBY ProgramWith a coverage of more than 171 million people at present, an

annual budget of $96 million (assuming US$ 1 = Rs. 50) in 2009–

10 [14,23], and a final target enrolment of 300 million by 2013,

RSBY is among the world’s largest health insurance programs.

Originally, households below the official poverty line (BPL) were

eligible to participate in RSBY. However, the government has

recently decided to include additional non-BPL but socioeconom-

ically disadvantaged groups under RSBY, such as domestic

workers, or workers in construction or beedi industries [24].

RSBY has a complex structure, with the involvement of several

stakeholders, and requiring intensive monitoring by the state

government. It is implemented by the state nodal agency, an

independent body established to implement the scheme in the

state. The nodal agency is typically the Department of Labour (in

almost half of the states), but may also be under the Ministry of

Health [25]. The scheme has pioneered a public-private partner-

ship approach, with state governments contracting out the

provision of insurance coverage to private insurance companies.

In exchange for a registration fee (Rs. 30 or US$0.6) paid to the

government, enrolled households are entitled to receive inpatient

healthcare coverage up to Rs. 30,000 or US$600 per year (for a

family of 5). The scheme also provides money for transport

charges at the rate of Rs. 100 (US$2) per visit, with an annual

maximum of Rs. 1000 (US$20). Households must renew their

enrolment in the scheme every year.

The central and state governments pay the remainder of the

premium for each enrolled family directly to the insurance

companies. Premium rates are determined at the level of a district,

through a tendering process among insurance providers by the

state government. The state government also empanels various

public and private healthcare providers under the scheme.

Financial transactions between a covered household and health-

care providers are ‘‘cashless’’, and payment is made on the basis of

a biometric RSBY Smart-card. Insurance coverage is only

applicable for inpatient care, and a beneficiary can purchase

these treatments and procedures at a government-fixed price from

the provider. Payment is made using the biometric card, i.e. the

insurance provider reimburses the treatment cost directly to the

healthcare provider.

The enrolment process begins with the state government

providing the insurer with an electronic list of eligible BPL

households. This list is prepared at the district level. In close

cooperation with a district level officer, the insurer then publicizes

Determinants of RSBY Implementation and Uptake


the enrolment schedule and a registration location. All members of

an eligible family must visit the enrolment centre, and are enrolled

in the presence of a government officer and an insurance company

representative. The list of households that have been enrolled is

then collected by the nodal agency. Later, it is maintained

centrally, and is the basis for financial transfers from the central

government to the state governments (at a 75:25 ratio, as

mentioned earlier). The scheme also provides for the inclusion of

intermediaries such as NGOs to provide grassroots outreach and

assist members in utilizing the services after enrolment [26,27].

Existing Literature on RSBY EnrolmentThe current literature on RSBY enrolment is rather limited.

While some studies have examined the enrolment patterns at

national and state levels, analyses at more disaggregated levels of

governance are relatively scarce. Furthermore, research examining

RSBY enrolment patterns have been mostly descriptive. In this

section, we will briefly discuss a few of these studies, along with the

findings related to uptake rates of similar community insurance

programs in other countries.

A working paper examining 24 districts across seven Indian

states, does not find any evidence of gender, age, or demographic

bias among RSBY’s enrolled population [28]. Based on village

level probit analysis, the author concludes that villages have a

higher probability of participation if there are more eligible

families, and better access to commercial facilities. The study also

shows that remote villages have lower enrolment rates. The results,

although limited by low explanatory power of the model, also

exhibit a positive association between the local capacity to provide

public health services and enrolment rates. The study speculates

on the roles of poor-quality BPL lists, inadequate logistical

planning and preparation, and selective enrolment (based on cost

of enrolment) by insurance companies in determining enrolment

rates, but does not provide any empirical evidence.

Using a larger sample of 145 districts during the first year of

RSBY, Swarup (2011) finds district-wise imbalances in enrolment

rates [29]. The descriptive analysis by the author largely attributes

these variations in enrolment rates to ‘‘defective and outdated’’

BPL lists provided by the state governments to insurance

companies, and concludes that the errors in the lists also produce

a gender bias, since they include only the names of male heads of

household. However, this could also be attributed to skewed

incentives for insurance companies because payment is provided

on enrolment per household instead of enrolment per individual

[30].

Dror and Vellakkal (2012) evaluate the financial burden of

RSBY, and its implications for enrolment [22]. The authors argue

that finance plays an important role in undertaking enrolment

drives. In order to scale up from the existing levels of enrolment,

and to maintain the financial viability of the scheme, the central

budget allocation for RSBY should be increased and the scheme

will have to attract a large above-poverty-line enrolment (i.e., those

who pay a non-subsidized premium).

Studies on community health insurance programs in other

countries reveal that the evidence on enrolment equity across

socioeconomic groups is mixed [31,32]. Jehu-Appiah et al. (2011)

conducts a household level analysis of the Ghana National Health

Insurance Scheme to determine that enrolment is influenced by

socioeconomic factors, health status, and social perceptions [33].

Basaza et al. (2007) argue that low enrolment under the Ugandan

Health Insurance Scheme is a result of limited community

involvement and outreach, a lack of trust in the management of

the scheme, and people’s inability to pay the premiums [34].

While the design and operation of RSBY differs from community

health insurance schemes abroad, some of the analytical results

point to similar factors associated with enrolment and participa-

tion gaps.

In summary, the literature on RSBY points to the importance of

three factors in raising enrolment rates: strengthening information

and educational campaigns [30,35], strengthening administrative

capabilities that lead to updated BPL lists and the involvement of

local administrators [28,29], and improving financial outlays [22].

Methods

1. Data and Descriptive StatisticsOur data come from two main sources. RSBY enrolment data

which are continually updated are obtained from the official

RSBY web portal [36]. District-level enrolment rate (ranging from

0 to 1) is calculated from these data as a ratio of the total number

of enrolled BPL families to the total number of target BPL families.

At the time of data collection, RSBY was either not rolled out, or

enrolment data were not available for all districts. For this study,

384 districts from 22 states and a union territory have a non-zero

enrolment rate. Zero-enrolment-rate districts exist in situations

where RSBY is either completely absent from the entire state

containing that district (e.g. Andhra Pradesh), or where RSBY is

being implemented in other districts of the state (e.g. Rajasthan).

Figures 1 and 2 illustrate this variation in participation and

enrolment rates across districts in India. Table 1 also presents the

state wise number of districts for which these administrative data

were available, along with the median RSBY enrolment rate. The

commencement date of the scheme in each district is also available

in the data, and is used to control for enrolment time in our

analysis, i.e. the number of days that the scheme has been in effect

in a particular district (from commencement to May, 2012).

We matched data from the RSBY web portal with data from the

District Level Household Survey (DLHS) 2007–2008 [37], a large

cross-sectional survey of more than 600,000 Indian households,

except for the state of Nagaland. DLHS collected household-level

information on various socioeconomic indicators such as caste,

religion, location (rural or urban) and ownership of several types of

assets. Due to the large sample size, estimates from DLHS are

representative at the district level. We use district level socioeco-

nomic characteristics of households, the economic status of villages

in terms of availability of roads, electricity, and public health

facilities and the administrative capacity at the local level

measured by the average number of schemes implemented per

village in a district as the determinants of RSBY enrolment in our

analysis.

Since a state’s participation in RSBY will likely depend on

political and institutional factors, we use additional data on the

state’s fiscal health as measured by the ratio of gross fiscal deficit to

state GDP (From Reserve Bank of India data [38]), institutional

environment or quality of governance as measured by a corruption

index (based on a mix of perception and experience surveys [39]),

and state political affiliation vis-a-vis the central government.

Table 2 presents summary statistics of our compiled dataset. In

total, 590 districts are included in our analysis, of which 384

districts across 22 states and one union territory (Chandigarh) have

a non-zero enrolment rate. Of the 601 districts in DLHS, 9

districts were omitted due to absence of village level infrastructure

information, and 2 districts in Andaman and Nicobar islands were

omitted because the corruption index data were not available.

2. Empirical ApproachThree different sets of models are estimated to determine the

factors affecting RSBY participation and enrolment. First, we



estimate a multivariate probit model of the binary indicator of the

RSBY participation (i.e. non-zero enrolment) for all 590 districts.

This is followed by an OLS model of RSBY enrolment rate, which

includes both zero and non-zero enrolment districts. Finally, we

examine the determinants of enrolment rate only among the 384

districts that participate in RSBY (i.e. non-zero enrolment).

Since RSBY has not yet been adopted in all states and districts

(i.e., some states and districts have zero enrolment), we tested for

self-selection bias on the OLS estimates using tests for endogeneity.

The results show very weak and non-robust evidence of self-

selection. The absence of any strong self-selection bias could be a

result of using districts as the unit of analysis, instead of using

households or individuals as units of analyses.

We include a range of socioeconomic, political, and institutional

factors as explanatory variables in our probit and OLS models.

From DLHS data, we include the district level share of rural

households, households with a female head, various castes

(scheduled castes, scheduled tribes, and other backward castes),

and religions (Muslim, Christian, Sikh, and Buddhists, as

compared with Hindus, the largest religious group) that could

impact participation and enrolment. To assess the effect of local

infrastructure, also included are district level shares of villages with

all-weather roads, electricity, and at least one public health facility.

To capture the effect of standard of living of the households in a

district, we first use a principal component analysis to create a

household asset index from DLHS (following Filmer and Pritchett

2001 [40]). The household variables that are used to create the

asset index are the possession of tangible assets, such as radio,

sewing machine, TV, bicycle, car, and others, along with

indicators of housing condition, such as construction quality,

availability of toilets, sources of drinking water, and type of

cooking fuel. Households in the entire sample are divided into five

Figure 1. RSBY participating districts. Source: RSBY website (www.rsby.gov.in) downloaded on 22 May 2012. Map created using GADM data.www.gadm.org.doi:10.1371/journal.pone.0066296.g001



quintiles or standard of living groups based on this asset index.

The shares of households that belong to quintiles 2 to 5 in a district

are then included among the explanatory variables.

Given the significant state budget required to implement RSBY,

a state’s fiscal health could play an important role in determining

the extent of its participation [22].We use the ratio of gross fiscal

deficit to gross state GDP, obtained from the Reserve Bank of

India data [37], as a possible determinant in our analysis. RBI

(2013) Table IV.3 provides the state level average GFD/GSDP

ratios for the years 2004–2008, 2010–2011 and 2011–2012. We

took an overall average over this period to derive the GFD/GSDP

ratios. For UTs not included in this table, we used the average

central government’s fiscal deficit to GDP ratio from www.

indiastat.com).

Anecdotal evidence suggests that the political affiliation of the

state government, relative to the party in power at the national

level, may be an important determinant of a state’s willingness to

participate in RSBY [41]. To capture this relationship, we

construct a state-level political affiliation index, which measures

a state’s political proximity to the ruling UPA coalition at the

centre.

The affiliation index reflects the political identity of the state

government as of May 2012, and has the following values: 0 = BJP

(Bharatiya Janata Party, the principal opposition party at centre);

1 = National Democratic Alliance (NDA) coalition partner;

2 = Indian National Congress (INC) is principal opposition

party/UPA coalition partner is principal opposition party at state;

3 = Neutral party; 4 = UPA coalition partner; 5 = ruling party at

centre, INC. NDA is a coalition of opposition parties at the center,

including BJP and seven other smaller political parties. Neutral

parties are not affiliated with either the NDA coalition or the UPA

Figure 2. Variation in RSBY enrolment rates. Source: RSBY website (www.rsby.gov.in) downloaded on 22 May 2012. Map created using GADMdata. www.gadm.org.doi:10.1371/journal.pone.0066296.g002



coalition. Broadly speaking, the closer the state government is to

the national government, the higher the value of the index.

From this index, two binary indicators for affiliation (for the i-th

state) to the ruling party or neutral party are constructed and

included in the right hand side of the regression:

Oppositioni~1 if affiliation index has values 0,1, or 2

0 otherwise

(

i

if affiliation index has value 3

0 otherwise

(

The inclusion of the two binary variables allows us to measure

the effect of a state’s proximity to opposition parties at the centre,

or its neutrality with respect to the UPA coalition at the centre.

Table 1. Number of RSBY participating districts by state.

StateNon-participatingdistricts

RSBY Participatingdistricts (with data)

Total no. of districts instate Median enrolment rate

Andhra Pradesh 23 23 0.000

Arunachal Pradesh 6 10 16 0.442

Assam 22 5 27 0.531

Bihar 37 37 0.550

Chhattisgarh 1 15 16 0.657

Goa 2 2 0.000

Gujarat 25 25 0.507

Haryana 20 20 0.416

Himachal Pradesh 12 12 0.821

Jammu & Kashmir 12 2 14 0.401

Jharkhand 22 22 0.489

Karnataka 2 25 27 0.514

Kerala 14 14 0.749

Madhya Pradesh 45 45 0.000

Maharashtra 7 28 35 0.518

Manipur 6 3 9 0.686

Meghalaya 2 5 7 0.434

Mizoram 8 8 0.676

Orissa 5 25 30 0.601

Punjab 20 20 0.501

Rajasthan 29 3 32 0.373

Sikkim 4 4 0.000

Tamil Nadu 30 30 0.000

Tripura 4 4 0.724

Uttar Pradesh 1 69 70 0.366

Uttarakhand 13 13 0.519

West Bengal 1 18 19 0.635

Andaman & Nicobar 2 2 0.000

Chandigarh 1 1 0.508

Dadra & Nagar Haveli 1 1 0.000

Daman & Diu 2 2 0.000

Delhi 9 9 0.000

Lakshadweep 1 1 0.000

Pondicherry 4 4 0.000

Total 217 384 601 0.515

Source: http://rsby.gov.in/, data downloaded on 22 May 2012. Note: Total no. of districts in states reflect those surveyed in DLHS 2007–08. Since the survey, additionaldistricts have been created, but are not mentioned in this table. Districts in Nagaland have not been included since no DLHS data are available for the state.doi:10.1371/journal.pone.0066296.t001



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While political affiliation reflects the relationship of state ruling

parties with the centre as of May 2012 (i.e. at the time of our

RSBY data collection), it is also important to incorporate any

changes in the affiliation (as a result of election) since the

conception of RSBY in 2008. To this end, we include a binary

variable for change as a covariate. Additionally, in the event that a

political change did occur, the swing in political affiliation is

captured by another variable measuring the difference in ‘before’

and ‘after’ political affiliations. For example, Goa was ruled by the

Congress from 2007 to 2012, and then went to the BJP. Based

upon the coding of our political affiliation index, Goa’s political

swing = –5. Table 3 presents an overview of the political affiliation

of Indian states.

The administrative capacity of a state’s bureaucracy may also

significantly influence its participation in RSBY. This is particu-

larly due to the complex structure of RSBY, as discussed in section

2. We try to capture this in our covariates in two ways. First,

DLHS collects data on the number of government schemes (from

a list of schemes) already implemented in each village. We

compute the average number of schemes implemented by villages

in each district, and use it as an indicator of the bureaucratic

efficiency of a district.

The level of corruption is another possible proxy for the quality

of governance. We use the Transparency International India–

Centre for Media Studies India (TII-CMS) corruption index as a

second measure for the quality of governance. These data are

publicly available at the state level from the report of the TII-CMS

India Corruption Survey 2007. The survey groups states into four

categories of the level of corruption: moderate, high, very high,

and alarming.

This corruption index is suitable for our analysis for two

reasons. First, the TII-CMS survey measures both corruption

perception and experience across 11 public services in various

sectors (including the public distribution system, hospitals,

electricity, water, and the National Rural Employment Guarantee

Scheme). Second, the survey measures not only perception, but

also the experience of BPL households in dealing with the

government.

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state) as below, has also been included among explanatory

variables:

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scheme may depend on whether the state has already implement-

ed, or is in the process of implementing, its own social health

insurance program. From various sources such as state agency

websites, we collected data on these state level schemes. We found

five states with such schemes: Andhra Pradesh (Rajiv Aarogyasri

Scheme), Goa (Mediclaim Scheme), Karnataka (Vajpayee Arogyasri

Scheme/Yesashwini), Kerala (Comprehensive Health Insurance

Scheme), and Tamil Nadu (Chief Minister Kalaignar Insurance

Scheme). In Kerala, the Comprehensive Health Insurance

Scheme covers the BPL families who are not covered by the

RSBY. Also, the insurance is available to non-BPL families at a

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Among other state health insurance schemes, Maharashtra

rolled out its Rajiv Gandhi Jeevandayee Arogya Yojana in July 2012, and

Rajasthan has recently announced Mukhyamantri BPL Jeevan Raksha

Kosh. Since our enrolment data are dated May 2012, we did not

consider Maharashtra or Rajasthan in our analysis. An indicator

of such state-level schemes, for the i-th state as below, is included in

our covariates:

The OLS models of enrolment rate include an additional

explanatory variable – a measure of enrolment time (in days),

which is the difference between the commencement date and the

date of our RSBY data collection. The OLS regressions are first

performed on all districts in the sample, and then on a restricted

sample of participating districts.

Results and Discussion

1. Probit Regression of RSBY ParticipationTable 4 presents the results from the probit and OLS regression

models. Results from the probit model show that some socioeco-

Table 3. State level political and corruption classifications.

States Ruling Party Affiliation Affinity level

Pol changebetween 2008–2012 Political Swing Corruption Index

Andhra Pradesh INC UPA 5 0 0 1

Arunachal Pradesh INC UPA 5 0 0 2

Assam INC UPA 5 0 0 4

Bihar JD(U) BJP NDA 1 0 0 4

Chhattisgarh BJP NDA 0 0 0 2

Goa BJP NDA 0 1 25 4

Gujarat BJP NDA 0 0 0 2

Haryana INC UPA 5 0 0 1

Himachal Pradesh BJP NDA 0 0 0 1

Jammu & Kashmir JKNC UPA 4 0 0 4

Jharkhand BJP NDA 0 1 0 2

Karnataka BJP NDA 0 0 0 3

Kerala INC UPA 5 1 5 2

Madhya Pradesh BJP NDA 0 0 0 4

Maharashtra INC UPA 5 0 0 1

Manipur INC UPA 5 0 0 2

Meghalaya INC UPA 5 1 2 3

Mizoram INC UPA 5 0 2 1

Orissa BJD – 2 0 0 2

Punjab SAD NDA 1 0 0 1

Rajasthan INC UPA 5 0 0 3

Sikkim SDF – 3 0 0 3

Tamil Nadu AIADMK – 2 1 22 3

Tripura CPI (M) – 2 0 0 1

Uttar Pradesh SP – 3 1 0 4

Uttarakhand INC UPA 5 1 5 1

West Bengal TMC UPA 4 1 2 1

Andaman & Nicobar UPA UPA 5 0 0 Not Available

Chandigarh UPA UPA 5 0 0 1

Dadra & Nagar Haveli UPA UPA 5 0 0 1

Daman & Diu UPA UPA 5 0 0 1

Lakshadweep UPA UPA 5 0 0 2

Delhi INC UPA 5 0 0 1

Pondicherry INRC UPA 5 0 0 1

Source: Political affiliation data compiled from various sources. Corruption Index is from TII-CMS 2007. Affiliation index coded as 0 = BJP (principal opposition party atcentre); 1 = National Democratic Alliance (NDA) coalition partner; 2 = Indian National Congress (INC) is principal opposition party/UPA coalition partner is principalopposition party, at state; 3 = Neutral party; 4 = UPA coalition partner; 5 = ruling party at centre, INC.doi:10.1371/journal.pone.0066296.t003



nomic characteristics of districts have a significant effect on a

district’s participation in RSBY. We observe that the districts with

more female-headed households have a greater probability of

participating in RSBY. This can be explained by a positive

association between female leadership at household or community

level, and access to public goods in India, as other studies have

pointed out. For example, Jalan and Ravallion (2003) [42] find

that female-headed households have greater access to piped water,

while Bhalotra and Clots-Figueras (2011) [43] show that state

assembly constituencies with women politicians are more likely to

have public health facilities, and better antenatal care and

immunization rates. Chattopadhyay and Duflo (2004) [44], and

Andersen et al. (2008) [45] also discuss the implications of female

leadership.

The importance of caste and religion as determinants of access

to public goods in India has been previously shown [46–49]. For

example, similar to the findings in Betancourt and Gleason (2000)

[46], we observe that districts with a higher share of socioeco-

nomically backward groups such as Scheduled Tribes (ST) and

Other Backward Castes (OBC) are less likely to participate in

RSBY. Given the stated pro-poor targeting of the RSBY, this is a

very important finding, which shows that there is a clear gap

between the policy and its practice. We also find that districts with

a higher share of Christian households are less likely to participate,

Table 4. RSBY Participation and Enrolment rate regression estimates.

Probit(Marginal Effect)

OLS(All districts)

OLS(Participating districts)

Model (I)Participation

Model (II)Enrolment rate

Model (III)Enrolment rate

Share of households that are/have:

Rural 20.343 20.000 0.139

Female Household head 0.925** 0.550*** 0.216

Scheduled Castes 20.511 20.268* 20.058

Scheduled Tribes 20.410* 20.210** 20.118

Other Backward Castes 20.356** 20.126* 20.073

Muslim 20.071 0.098 0.151*

Christian 20.396** 0.166* 0.183**

Sikh 1.481** 20.220*** 20.232***

Buddhist 20.131 20.027 20.105

Share of households that belong to:

Wealth Quintile 2 0.695** 0.168 20.120

Wealth Quintile 3 0.113 20.418*** 20.527***

Wealth Quintile 4 0.636* 0.265* 0.160

Wealth Quintile 5 20.108 20.225* 20.264*

Share of villages per district that have:

All weather roads 0.123 0.153** 0.098

Electricity 20.624*** 20.103* 0.043

Public health facilities 20.004 20.001 20.034

Average no. of schemes implemented 0.007 20.002 20.002

Enrolment time (days) 0.001*** 0.000

Fiscal Health (Gross Fiscal Deficit/GSDP) 0.045** 0.005 0.012*

Corruption Index 20.208*** 20.103*** 20.061***

Opposition Party 0.435*** 0.247*** 0.117***

Neutral Party 0.210*** 0.186*** 0.125**

Any political change (2008–2012) 0.178*** 20.073*** 20.121***

Political swing 0.293*** 0.045*** 0.034***

State Insurance Scheme 20.234** 20.042 0.056

Constant 0.404*** 0.510***

Psuedo R2/R2 0.531 0.619 0.262

Adjusted R2 0.603 0.210

Number of districts 590 590 384

Source: RSBY website (www.rsby.gov.in) for enrolment data, downloaded on 22 May 2012. DLHS 2007–2008 for socioeconomic data, RBI (2013) for state fiscal data, andcorruption index is from CII-TMS (2007). Coefficients that are statistically significant at 10%, 5%, and 1% level are marked with *, **, and *** respectively. Huber-Whiterobust standard errors are used in all regressions.doi:10.1371/journal.pone.0066296.t004



and districts with a higher share of Sikhs are more likely to

participate in the program.

The correlation between standard of living variables and RSBY

enrolment is not monotonic. Districts with more households in

second and fourth wealth quintiles are more likely to participate in

RSBY, compared to the poorest wealth quintile. This may indicate

a so called ‘elite capture’, whereby the richer districts, by virtue of

administrative and political clout, successfully draws resources

from the program [49,50]. Alternatively, a median district may be

more efficient in implementing RSBY, as opposed to districts with

a higher share of BPL households (i.e. those in the lowest quintile)

who may not have the resources to implement RSBY, while

districts with a large number of households in in quintile 5 may be

too rich to merit participation.

The inconsistent targeting, as shown by our results, is a very

important finding. Various studies have noted the poor quality of

BPL lists in the context of RSBY, e.g. the lists are often outdated,

or include non-eligible households while excluding eligible ones

[29,30]. While this cannot be directly tested, our results provide an

indirect way to evaluating the pro-poor targeting. If the BPL lists

were consistent, one would expect a consistently negative effect of

our wealth status variables, with the effect size growing over

quintiles.

Districts with a higher share of villages that are electrified are

less likely to participate in the scheme, but the availability of all-

weather roads or public health facilities do not have any significant

impact. Sun (2010) [28] refers to the initial effect of power cuts in

delaying RSBY enrolment, and how providers adapt by equipping

themselves with power backup and generators. The negative

association between electricity and enrolment seen in our results

may indicate pro-poor targeting, whereby the scheme is first rolled

out among resource-constrained villages. Furthermore, adminis-

trative capacity, as measured by the average number of schemes

already being implemented in villages, has no significant effect on

the likelihood of a district participating in the program. Another

possible factor in the efficacy of implementation could be the nodal

agency in the state. We find some additional evidence (not

presented in the results), that the Department of Labour is

associated with higher enrolment rates versus other nodal

agencies. However, there does not seem to be any effect on the

likelihood of participation.

Analysing the effect of state finances on participation, we find

that districts in states with a higher ratio of gross fiscal deficit to

gross state GDP are more likely to participate in the scheme. This

may be due to two of reasons. First, states with poorer fiscal health

may have a higher share of BPL population, and therefore may

attract more resources. This residual effect may exist even after

controlling for standard of living (wealth quintiles), since there are

inconsistencies in the BPL lists. Secondly, a higher fiscal deficit

may already reflect a predisposition to implementing expensive

welfare measures. Both of these will make such a state more likely

to implement RSBY. Since the fiscal deficit may not accurately

reflect the quality of state expenditure, we also measured the

relationship between Non Development Revenue Expenditure

(NDRE) of the state and participation, to conclude that states with

greater levels of NDRE are less likely to participate in the scheme.

Among the political variables, we find that districts located in

states that are ruled by parties closely affiliated with the ruling

UPA coalition at the centre are significantly less likely to

participate in RSBY. There is currently no analytical evidence

for the effect of political affiliation on RSBY participation,

although this relationship has been previously mentioned in media

outlets [41]. In fact, the union labour minister of India recently

claimed that three major Congress ruled states, Andhra Pradesh,

Rajasthan, and Maharashtra, were making inadequate efforts to

implement RSBY [51]. Analysing his comments further, we note

that all three states have pre-existing, or planned, state level health

insurance schemes. Our results show that such states are less likely

to participate in RSBY, as discussed later in this section.

Therefore, Congress rule by itself may not explain the negative

association between UPA-allegiance and RSBY participation.

There may be additional political factors, at a more disaggregated

level, which are systematically different between the UPA-affiliated

states and others. Sun (2010) [28], discusses the importance of

lower level politics and the role of leaders in attracting enrolment

camps to villages with a greater proportion of BPL families. The

author implies that villages with a larger number of BPL families

may be able to attract enrolment camps due to strong political ties

between the village administration and higher levels of authority.

Unfortunately, due to data paucity, the analysis of such factors is

beyond the scope of this study.

Our results indicate that there may not be any political stigma

associated with implementing a scheme championed by another

political party. Even controlling for political changes in the interim

period 2008–2012, i.e. after the official commencement of RSBY,

we find that the political affiliation results are robust. Furthermore,

districts in states, which witness a political change during 2008–

2012, are more likely to participate in the scheme. Finally, we see

that given a political change, districts in states that shifted closer

towards the ruling coalition are also more likely to take up the

program.

We find a negative association between higher levels of

corruption, as measured by the corruption index, and RSBY

participation. It echoes the current empirical evidence on the ill

effects of corruption on the provision of public goods [52,53,54].

Over and above the conventional explanation of a lack of capacity

to deliver a program, the negative association between corruption

and participation may also reflect a lack of confidence among

households in the administrative capability of the local govern-

ment. Therefore, the perceived utility of participating in the

program will be low, as seen in the case of some other schemes

around the world [33].

Note however, a possible counter argument is that states with

higher level of corruption may be more inclined to implement a

scheme such as RSBY. Given the very high level of central funding

(at least 75%), RSBY may provide an opportunity for further

corruption. Therefore, the negative effect of corruption, as seen in

our results, may be an underestimation. Furthermore, although

the corruption measure used in this paper - a mix of perception

and experience would be better that an index solely based on

perception, systematic differences in self-reporting across popula-

tion subgroups cannot be captured in our study.

We also find that states with their own pre-existing health

insurance programs are significantly less likely to participate in

RSBY. As mentioned in [14],) the five states with their own health

insurance schemes for the poor have already budgeted substantial

sums of money (e.g. the Rajiv Aarogyasri scheme in Andhra Pradesh

allocated $21.5 million in 2009–10), and in some cases have more

ambitious goals (covering more ailments and population seg-

ments). It is therefore not a surprise that these states are less likely

to participate in RSBY.

2. OLS Regression of RSBY Enrolment RateThe last two columns in Table 4 present OLS estimates of

RSBY enrolment rates. The first set of estimates is based on a

sample of all districts, and results in the last column are from a

sample of participating districts only. We observe that similar to

the probit model, factors such as caste and gender of the



household head are statistically significant determinants of

enrolment rates. In particular, districts with a higher share of

SC, ST, or OBCs have a lower enrolment rate in model (II),

although none of these caste variables (or household head’s

gender) are significant in model (III). We also find some significant

effect of religion.

In contrast with estimates for participation, the wealth quintile

coefficients in the enrolment regression models indicate stronger

pro-poor patterns. We see that districts with a high share of

households in wealth quintiles 3 and 5 are associated with lower

rates of enrolment in comparison with households in quintile 1.

Infrastructure in villages plays a significant role in explaining

enrolment rates in model (II). Districts with a greater share of

villages connected by all-weather roads are associated with higher

rates of enrolment, while districts with a larger proportion of

villages with electricity have lower enrolment rates. However,

conditional on participation, these factors do not have any

significant effect on enrolment rates, as seen in model (III).

The association between state fiscal health and enrolment

appears to be weak or insignificant, but political and institutional

factors remain very strong determinants of enrolment rates.

Similar to model (I), corruption index is negatively associated with

higher enrolment rates, and districts in ruling party states appear

to have lower enrolment rates in comparison to others.

However, we find a negative effect of political change in the

period 2008–2012 on enrolment rates in both models. When seen

together with the results in model (I), this coefficient implies that

while a change in the state government is associated with a higher

likelihood of participating in RSBY (regardless of the affiliation of

the new government), the political change may be detrimental to

lower level bureaucracy, which controls the RSBY enrolment.

There may be a lag in the information flow between the state

government and administration at lower levels, controlling for

time elapsed after enrolment commences in a district, which may

thwart enrolment activities.

The coefficients of political swing imply that given a state

undergoes a political change; higher enrolment rates are associated

with states affiliated with the ruling UPA at the centre. Finally,

pre-existing state insurance schemes have no statistically significant

impact on the enrolment rates, and the time elapsed since the first

implementation of RSBY in a district has some positive effect on

enrolment in model (II).

It is important to note some limitations of our study. For

example, we assume that the central government always provides

full support for RSBY, and it is the state government that may

choose to not roll out the program. However, as Dror and

Vellakkal (2012) [22] point out, the budget allocation of the central

government for RSBY falls well short of the projected expenditure,

and the centre would find it difficult to adequately fund the

scheme if all state governments were to fully embrace it. The

authors also argue that premium rates are artificially low, often

leading to a lack of enthusiasm from insurance providers. And,

there may also be a discontent among the healthcare providers, as

the government-fixed uniform prices for services are very low for

some areas.

Secondly, due to lack of data, we cannot incorporate the role of

some demand side factors in our study. In particular, as Das and

Leino (2011) [30] point out, informational campaigns may have an

important role to play in raising the awareness about RSBY. We

assume that since the RSBY nodal agency and the state

government are in charge of these campaigns, resources spent

for such campaigns will be captured by other state level variables

(e.g. fiscal health and standard of living) in our analysis.

Thirdly, although our results related to standard of living and

socioeconomically backward groups show imprecise targeting,

they are not a clear indicator of the problem related to BPL lists

[28,29]. Due to lack of data, we cannot directly evaluate the issue

with these lists.

Finally, while the enrolment data are from 2012, the DLHS

data are from 2007–08. This implies that there may be time

varying factors, which may affect our results. Even though we

include a measure of time elapsed since RSBY inception, as an

explanatory variable, any large changes in other covariates,

although unlikely, remain unobserved.

ConclusionThe success of RSBY in achieving high enrolment rates is

impressive, in comparison to much lower enrolment rates typically

seen in other developing country [55,56]. Our paper, which

accounts for major socioeconomic, political, and institutional

factors, points to some important conclusions. First, we find

inequities in participation and enrolment by caste and religious

groups. In particular, districts with socially backward communities

(scheduled castes and scheduled tribes) experience lower partici-

pation and enrolment rates, and we also find some possible

evidence of benefits being captured by higher wealth groups.

These results reveal the weak nature of the pro-poor targeting

mechanism of RSBY. Using these findings as a benchmark, the

RSBY nodal agencies and state governments should strengthen the

enrolment system, to align the resources with the needs of the

people.

Secondly, institutional and political factors seem to be strongly

correlated with both participation and enrolment rates. Districts in

states with weaker administrative capacity, or those with pre-

existing state health insurance program, are less likely to take up

the RSBY program. Counterintuitively, opposition and non-UPA-

allied states seem to be embracing the program far more

enthusiastically than UPA-ruled states. The reasons for the lack

of enthusiasm, though, are not clear (barring some speculative

statements in newspaper articles) and merit further research.

Also, the impact of RSBY on improving population health, or

reducing financial impoverishment, remains to be seen. The

program faces significant challenges in its operating environment

towards that objective. Supply-side problems include severe

resource shortages (manpower, sanitation infrastructure, hospital

beds, quality medicines), or physical distance from facilities. On

the other hand, demand side issues such as costs or lack of

awareness may result in underutilization of health services,

especially in poor and marginalized communities [57].

Finally, there is a need for more disaggregated enrolment and

claims data, which could offer useful insights about the time trends

of participation, enrolment and utilization. While there is some

research on the major determinants of RSBY utilization [12],

more research is required to justify a large scale program such as

RSBY. As Fan and Mahal (2011) [58] argue, there is a dire need

for structured evaluation of health policies in India, a process that

engages various stakeholders such as governments and the

researcher community, facilitates flow of knowledge between the

involved groups, and encourages evidence-based policymaking.

Author Contributions

Analyzed the data: AN AA RL. Wrote the paper: AN AA RL.



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